{"schema_version":"onlylabs.public_signal.v1","title":"LG AI Research (EXAONE) Model: LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","description":"LG AI Research (EXAONE) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/506c66b8-350f-45c2-9004-9d5d74a7110f","json_url":"https://onlylabs.fyi/signals/506c66b8-350f-45c2-9004-9d5d74a7110f/signal.json","generated_at":"2026-06-11T03:03:58.313462+00:00","org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/lg-ai","dossier_json_url":"https://onlylabs.fyi/labs/lg-ai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/506c66b8-350f-45c2-9004-9d5d74a7110f","signal_json":"https://onlylabs.fyi/signals/506c66b8-350f-45c2-9004-9d5d74a7110f/signal.json","source":"https://huggingface.co/LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","lab_dossier":"https://onlylabs.fyi/labs/lg-ai","lab_dossier_json":"https://onlylabs.fyi/labs/lg-ai/dossier.json","analysis":"https://onlylabs.fyi/analysis/lg-ai","analysis_json":"https://onlylabs.fyi/analysis/lg-ai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/lg-ai/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"LG AI Research (EXAONE) published LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor. 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for this category, keep conclusions tied to category-specific strategy, source evidence, and follow-up questions.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/506c66b8-350f-45c2-9004-9d5d74a7110f/signal.json","required":true},{"label":"source","url":"https://huggingface.co/LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/lg-ai/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/lg-ai/evidence.json","required":true},{"label":"topic_signals_json","url":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited evidence, analyze LG AI Research (EXAONE)'s model signal \"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor\" for neolab strategy."},"semantic_triples":[{"subject":"LG AI Research (EXAONE)","predicate":"published model","object":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","text":"LG AI Research (EXAONE) published model LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"is classified as","object":"model signal","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor is classified as model signal."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"belongs to","object":"releases desk","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor belongs to releases desk."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has evidence coverage","object":"1 captured evidence page","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has evidence coverage 1 captured evidence page."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has captured page count","object":"1","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has captured page count 1."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has readable page count","object":"1","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has readable page count 1."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has related signal count","object":"6","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has related signal count 6."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has analysis playbook objective","object":"Turn model cards, repository releases, and launch artifacts into a current view of what shipped and what changed.","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has analysis playbook objective Turn model cards, repository releases, and launch artifacts into a current view of what shipped and what changed.."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has source host","object":"huggingface.co","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has source host huggingface.co."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has lab","object":"LG AI Research (EXAONE)","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has lab LG AI Research (EXAONE)."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has signal desk","object":"releases","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has signal desk releases."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has source host","object":"huggingface.co","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has source host huggingface.co."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has license","object":"other","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has license other."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has downloads","object":"0","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has downloads 0."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has likes","object":"5","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has likes 5."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has notability","object":"Specialized model release, not major traction","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has notability Specialized model release, not major traction."},{"subject":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor","predicate":"has watch term","object":"Model card","text":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor has watch term Model card."}]},"intelligence":{"signal_desk":"releases","answer":"LG AI Research (EXAONE) published LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor. 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When the model receives an H&E-stained whole slide image as input, it removes artifacts observed in the image and extracts only tissue-related objects. These objects are then reconstructed into a set of tiles with a size of 256 by 256 pixels at an mpp (micron per pixel) of 0.5. The tiles pass through the **EXAONEPath v1.0 patch-level foundation model** (https://huggingface.co/LGAI-EXAONE/EXAONEPath), which converts them into a set of features. These features are then integrated into a slide-level feature representation through an aggregator(see the figure below). Finally, a linear classifier predicts the MSI status (MSS or MSI-H/L). The model achieves an average performance of AUROC 0.93 on TCGA-COAD + TCGA-READ data and 0.84 on in-house data. This open-source release aims to demonstrate that the combination of EXAONEPath..."},"evidence_pages":[{"url":"https://huggingface.co/LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor/raw/main/README.md","final_url":"https://huggingface.co/LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor/raw/main/README.md","title":"LGAI-EXAONE/EXAONEPath-CRC-MSI-Predictor model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T03:03:58.313462+00:00","bytes":2585,"raw_path":"f76a88bcf7ff78e7af796a19e2f788335b9f71c5c20eab0d2e4d8550b3026b62.md","content_hash":"cc124958a26f2497c0efd7359465e07333124a8073a8f79f659ab182602ef36f","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: other license_name: exaonepath license_link: LICENSE tags: - lg-ai - EXAONEPath-1.0 - pathology - MONAI --- Description MSI classification of CRC tumors using EXAONEPath 1.0.0 - a patch-level foundation model for pathology Model Overview This model serves as a reference for predicting MSI status using CRC (colorectal cancer) tumor images as input. When the model receives an H&E-stained whole slide image as input, it removes artifacts observed in the image and extracts only tissue-related objects. These objects are then reconstructed into a set of tiles with a size of 256 by 256 pixels at an mpp (micron per pixel) of 0.5. The tiles pass through the **EXAONEPath v1.0 patch-level foundation model** (https://huggingface.co/LGAI-EXAONE/EXAONEPath), which converts them into a set of features. These features are then integrated into a slide-level feature representation through an aggregator(see the figure below). Finally, a linear classifier predicts the MSI status (MSS or MSI-H/L). The model achieves an average performance of AUROC 0.93 on TCGA-COAD + TCGA-READ data and 0.84 on in-house data. 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